The present invention first relates to an encoding method for the compression of a video sequence including successive frames organized in groups of frames, each frame being decomposed by means of a three-dimensional (3D) wavelet transform leading to a given number of successive resolution levels, said encoding method being based on the hierarchical subband encoding process called xe2x80x9cset partitioning in hierarchical treesxe2x80x9d (SPIHT) and leading from the original set of picture elements (pixels) of each group of frames to wavelet transform coefficients encoded with a binary format and constituting a hierarchical pyramid, said coefficients (i,j) being organized into a spatio-temporal orientation tree rooted in the lowest frequency (or approximation subband) resulting from the 3D wavelet transform and completed by an offspring in the higher frequency subbands, the coefficients of said tree being ordered into partitioning sets involving the pixels and corresponding to respective levels of significance, said sets being defined by means of magnitude tests leading to a classification of the significance information in three ordered lists called list of insignificant sets (LIS), list of insignificant pixels (LIP) and list of significant pixels (LSP), said tests being carried out in order to divide said original set of pixels into said partitioning sets according to a division process that continues until each significant coefficient is encoded within said binary representation, and said spatio-temporal orientation tree defining the spatio-temporal relationship inside said hierarchical pyramid.
The invention also relates to an encoding method for the compression of a video sequence including successive frames organized in groups of frames, each frame being decomposed by means of a three-dimensional (3D) wavelet transform leading to a given number of successive resolution levels, said encoding method being based on a hierarchical subband encoding process leading from the original set of picture elements (pixels) of each group of frames to wavelet transform coefficients constituting a hierarchical pyramid, a spatio-temporal orientation tree-in which the roots are formed with the pixels of the approximation subband resulting from the 3D wavelet transform and the offspring of each of these pixels is formed with the pixels of the higher subbands corresponding to the image volume defined by these root pixelsxe2x80x94defining the spatio-temporal relationship inside said hierarchical pyramid, said subbands being scanned one after the other in an order that respects the parent-offspring dependencies formed in said spatio-temporal tree, and flags xe2x80x9coff/onxe2x80x9d being added to each coefficient (i,j) of said spatio-temporal tree in view of a progressive transmission of the most significant bits of the coefficients and in such a manner that at least one of them describes the state of a set of pixels and at least another one describes the state of a single pixel.
The expansion of multimedia applications is now making the scalability one of the most important functionalities of video compression schemes. Scalability allows delivering multiple levels of quality or spatial resolutions/frame rates in an embedded bitstream towards receivers with different requirements and encoding capabilities. Current standards like MPEG-4 have implemented scalability in a predictive DCT-based framework through additional high-cost layers. More efficient solutions based on a three-dimensional wavelet decomposition followed by a hierarchical encoding of the spatio-temporal trees like the Set Partitioning In Hierarchical Trees algorithm (SPIHT) have been recently proposed as an extension of still image coding techniques (the original SPIHT algorithm is described for instance in xe2x80x9cA new, fast, and efficient image codec based on set partitioning in hierarchical treesxe2x80x9d, by A. Said and W. A. Pearlman, IEEE Transactions on Circuits and Systems for Video Technology, vol. 6, nxc2x03, June 1996, pp. 243-250, and the extension of this algorithm to the 3D case is described for instance in xe2x80x9cAn embedded wavelet video coder using three-dimensional set partitioning in hierarchical trees (SPIHT)xe2x80x9d, B. J. Kim and W. A. Pearlman, Proceedings of Data Compression Conference, Mar. 25-27, 1997, Snowbird, Utah, USA, pp.251-260). The 3D wavelet decomposition provides a natural spatial resolution and frame rate scalability, while the in-depth scanning of the obtained coefficients in the hierarchical trees and the bitplane encoding lead to the desired quality scalability with a high compression ratio.
The SPIHT algorithm is based on a key concept: a partial sorting of the coefficients according to a decreasing magnitude, and the prediction of the absence of significant information across scales of the wavelet decomposition by exploiting self-similarity inherent in natural images. This means that if a coefficient is insignificant at the lowest scale of the wavelet decomposition, the coefficients corresponding to the same area at the other scales have a high probability to be insignificant too. Basically, the SPIHT is an iterative algorithm that consists in comparing a set of pixels corresponding to the same image area at different resolutions with a value called xe2x80x9clevel of significancexe2x80x9d from the maximal significance level found in the spatio-temporal decomposition tree down to 0. For a given level, or bitplane, two passes are carried out: the sorting pass, which looks for zero-trees or sub-trees and sorts insignificant and significant coefficients, and the refinement pass, which sends the precision bits of the significant coefficients. The SPIHT algorithm examines the wavelet coefficients from the highest level of the decomposition to the lowest one. This corresponds to first considering the coefficients corresponding to important details located in the smallest scale subbands, with increasing resolution, then examining the smallest coefficients, which correspond to fine details. This justifies the xe2x80x9chierarchicalxe2x80x9d designation of the algorithm: the bits are sent by decreasing importance of the details they represent, and a progressive bitstream is thus formed.
A tree structure, called spatial (or spatio-temporal in the 3D case) orientation tree, defines the spatial (or spatio-temporal) relationship inside the hierarchical pyramid of wavelet coefficients. The roots of the trees are formed with the pixels of the approximation subband at the lowest resolution (xe2x80x9crootxe2x80x9d subband), while the pixels of the higher subbands corresponding to the image area (to the image volume, in the 3D case) defined by the root pixel form the offspring of this pixel. In the 3D version of the SPIHT algorithm, each pixel of any subband but the leaves has 8 offspring pixels, and each pixel has only one parent. There is one exception at this rule: in the root case, one pixel out of 8 has no offspring. The following notations describe the parent-offspring relationship, an illustration of these dependencies being given in FIG. 1 (three-dimensional case) where the notations are the following: TF=temporal frame, TAS=temporal approximation subband, CFTS=coefficients in the spatio-temporal approximation subbands (or root coefficients), TDS.LRL=temporal detail subband at the last resolution level of the decomposition, and TDS.HR=temporal detail subband at higher resolution:
O(x,y,z): set of coordinates of the direct offspring of the node (x,y,z);
D(x,y,z): set of coordinates of all descendants of the node (x,y,z);
H(x,y,z): set of coordinates of all spatio-temporal orientation tree roots (nodes in the highest pyramid level: spatio-temporal approximation subband);
L(x,y,z)=D(x,y,z)xe2x88x92O(x,y,z).
The SPIHT algorithm makes use of three lists: the LIS (list of insignificant sets), the LIP (list of insignificant pixels), and the LSP (list of significant pixels). In all these lists, each entry is identified by a coordinate (x,y,z). In the LIP and LIS, (x,y,z) represents a unique coefficient, while in the LIS it represents a set of coefficients D(x,y,z) or L(x,y,z), which are sub-trees of the spatio-temporal tree. To differentiate between them, the LIS entry is of type A if it represents D(x,y,z), and of type B if it represents L(x,y,z). During the first pass (sorting pass), all the pixels of the LIP are tested and those that become significant are moved to the list LSP. Similarly, the sets of the LIS that become significant are removed from the list LIS and split into subsets that are placed at the end of the LIS and will be each examined in turn. The LSP contains the list of significant pixels to be xe2x80x9crefinedxe2x80x9d: the nth bit of the coefficient is sent if this one is significant with respect to the level n.
The SPIHT approach is designed to provide quality scalability associated with a high compression ratio. However, scalability in temporal or spatial resolutions cannot be obtained with this coding strategy without modifications. To improve the global compression rate of the video coding system, it is usually advised to add an arithmetic encoder to the zero-tree encoding module.
To make the arithmetic coding efficient, it is very important to capture all the information that may have some influence on the current pixel and particularly the information related to neighbouring pixels. This information is represented by its context. The in-depth search performed when scanning for zero-trees does not exploit the redundancy inside subbands and makes harder the determination of a relevant context for the arithmetic coding. The manipulation of the lists LIS, LIP, LSP conducted by a set of logical conditions makes the order of pixel scanning hardly predictable. The pixels belonging to the same 3D offspring tree but coming from different spatio-temporal subbands are encoded and put one after the other in the lists, which has for effect to mix the pixels of foreign subbands. Thus, the geographic interdependencies between pixels of the same subband are lost. Moreover, since the spatio-temporal subbands result from temporal or spatial filtering, the frames are filtered along privileged axes that give the orientation of the details. This orientation dependency is also lost when the SPIHT algorithm is applied, because the scanning does not respect the geographic order.
It has then been proposed, in a previous european patent application filed on May 3, 2000, by the applicant under the number 00401216.7, a new strategy for encoding the spatio-temporal wavelet coefficients, inspired from the 3D-SPIHT, but which allows a better context selection while allowing to obtain a spatial or temporal resolution scalability in the coding scheme. According to said previous patent application, the proposed algorithm scans the subbands one after the other in an order that respects the parent-offspring relationships formed in the spatio-temporal tree, and flags off/on are added to each coefficient of the spatio-temporal tree, in order to constitute, in view of a progressive transmission of the most significant bits of the coefficients, three virtual magnitude-ordered lists LIS, LIP and LSP. These flags are such that at least one of them describes the state of a set of pixels and at least another one describes the state of a single pixel.
By using this technique, the initial subband structure of the
3D wavelet transform is preserved, and the flag added to each coefficient indicates to which list LIS, LIP or LSP this coefficient belongs. However, when using said modified SPIHT algorithm, most of the computation time is spent for the determination of the significance level of a set of pixels: all the spatio-temporal trees or subtrees are indeed examined as many times as they are insignificant relatively to the given level of significance, and the same costly operation is repeated for each level.
It is therefore an object of the invention to propose a less costly encoding method, with a reduced computation time.
To this end, the invention relates to a method such as defined in the introductory part of the description and which is moreover characterized in that, according to the algorithm indicated in the appendix A:
(a) starting by the pixels in the highest subbands and finishing by those belonging to the lowest ones, a full exploration of the subbands is performed during the initialization step of the process, and the set significance level SSL of each subtree of said tree in the root pixels is calculated and stored, according to the following procedure:
if the coefficient (i,j) has no child, SSL(i,j)=xe2x88x921;
else SSL(i,j)=max {PSL(k,l), SSL(k,l)};
(with k and lxcex5O(i,j), the offspring corresponding to the coefficient (i,j), and PSL is the pixel significance level given by:
PSL(i,j)=floor(log2xc3x97(i,j)),
(b) in the sorting step of the process, a comparison between SSL(i,j) and the current significance level n replaces the call to the function that computes the significance of a tree relatively to n:
if SSL(i,j) less than n, the subtree whose the root is (i,j) is insignificant relatively to n, else, the subtree is significant.
In a slightly distinct embodiment of the invention, the invention also relates to a method such as defined in the introductory part and which is moreover characterized in that, according to the algorithm indicated in the appendix B:
(a) starting by the pixels in the highest subbands and finishing by those belonging to the lowest ones, a full exploration of the subbands is performed during the initialization step of the process, the set significance level SSL of each subtree of said tree in the root pixels is calculated, and a flag is used to store for each coefficient the value of the corresponding set significance level SSL, according to the following procedure:
if the coefficient (i,j) has no child, SSL(i,j)=xe2x88x921;
else SSL(i,j)=max {PSL(k,l), SSL(k,l)};
(with k and lxcex5O(i,j), the offspring corresponding to the coefficient (i,j), and PSL is the pixel significance level given by:
PSL(i,j)=floor(log2|xc3x97(i,j)|),
(b) in the sorting step of the process, a comparison between SSL(i,j) and the current significance level n replaces the call to the function that computes the significance of a tree relatively to n:
if SSL(i,j) less than n, the subtree whose the root is (i,j) is insignificant relatively to n, else, the subtree is significant.
Whatever its implementation, this improved method, that may be applied to any coding algorithm based on the determination of zero-trees, highly reduces the computation time of the main loop of the algorithm. An increase in memory size is observed, but is relatively small.